I would like to start by saying that I have been a passionate admirer and intensive user of OntoNotes for a long time, but I also suspect that dissatisfaction with some aspects of its annotation scheme is widespread: this is evidenced by the emergence of the Coreference Resolution Beyond OntoNotes workshops since 2016 (CORBON, and later CRAC, `Computational Models of Reference, Anaphora and Coreference'), the continuing creation of resources with different annotation schemes (cf. [1]}, [2]}; see [3]} for an overview), the focus of the 2021 coreference shared task on non-ON data, and most recently the launching of a Universal Anaphora (UA) initiative, aimed at bringing some consistency into the growing variety of divergences between resources.See http://universalanaphora.org/ However, I suspect that while we as a research community can arrive at a consensus about this dissatisfaction, it will be much harder to reach a consensus about what a solution might look like, and how it could be attained feasibly. My goal in writing this overview is therefore first to outline some of the reasons why we need to do something, and my audience is, in the first instance, the community working on language resources for coreference, and in the second instance, NLP researchers, and especially grad students, who are entering the field and may get the impression that the ON standard represents a final end point of what we want from coreference resolution. The suggestions below probably form a continuum between more and less controversial, and their ranking on different researchers' wish lists will differ as well based on their respective goals, but it's a discussion we can only have if the commonalities and differences between coreference datasets and task definitions are laid out clearly.For readers who prefer to start with worked examples, see the Appendix for some analyses contrasting the ON view of coreference with a more unrestricted annotation scheme.
